TY - GEN
T1 - Exploration of Bare-Hand Mid-Air Pointing Selection Techniques for Dense Virtual Reality Environments
AU - Shi, Rongkai
AU - Zhang, Jialin
AU - Yue, Yong
AU - Yu, Lingyun
AU - Liang, Hai Ning
N1 - Funding Information:
We thank the volunteers who participated in the user study. We also thank the reviewers for their valuable time and insightful comments that helped improve our paper. This research was partly funded by Xi’an Jiaotong-Liverpool University Special Key Fund (#KSF-A-03), the National Science Foundation of China (#62272396), and Suzhou Municipal Key Laboratory for Intelligent Virtual Engineering (#SZS2022004).
Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - Target selection in dense virtual reality (VR) environments is challenging. Prior work has explored different controller-based raycasting techniques to assist target selection in such environments. However, limited research has focused on selection via mid-air barehand, which represents another major input metaphor for immersive environments. In this paper, we first review the existing raycasting selection techniques for dense VR environments. Based on this, we propose and develop two freehand pointing selection techniques - HandDepthCursor and HandConeGrid, and implement MultiFingerBubble, a recently-proposed technique. We then conduct a user study to compare and evaluate their performance and experience in a target selection task in dense VR environments. Our results suggest that HandDepthCursor and HandConeGrid led to significantly faster and more accurate selection performance, and lower perceived workload and arm fatigue. In addition, HandConeGrid showed a distinct advantage in high-density environments.
AB - Target selection in dense virtual reality (VR) environments is challenging. Prior work has explored different controller-based raycasting techniques to assist target selection in such environments. However, limited research has focused on selection via mid-air barehand, which represents another major input metaphor for immersive environments. In this paper, we first review the existing raycasting selection techniques for dense VR environments. Based on this, we propose and develop two freehand pointing selection techniques - HandDepthCursor and HandConeGrid, and implement MultiFingerBubble, a recently-proposed technique. We then conduct a user study to compare and evaluate their performance and experience in a target selection task in dense VR environments. Our results suggest that HandDepthCursor and HandConeGrid led to significantly faster and more accurate selection performance, and lower perceived workload and arm fatigue. In addition, HandConeGrid showed a distinct advantage in high-density environments.
KW - bare-hand interaction
KW - dense environment
KW - head-mounted displays
KW - object selection
KW - pointing selection
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85158133188&partnerID=8YFLogxK
U2 - 10.1145/3544549.3585615
DO - 10.1145/3544549.3585615
M3 - Conference Proceeding
AN - SCOPUS:85158133188
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Y2 - 23 April 2023 through 28 April 2023
ER -